Rayleigh Mixture Model and its Application for Rayleigh Mixture Model and its Application for Ultrasound-based Plaque CharacterizationUltrasound-based Plaque Characterization

José Seabra, Francesco Ciompi, Oriol Pujol, Petia Radeva and João Sanches

Instituto de Sistemas e Robótica, IST LisboaCentre de Visió per Computador, Barcelona

Workshop Programa Doutoral em Engenharia Biomédica

15 Julho 2009

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

Vulnerable plaques are a major source of carotid and coronary circulatory events

B-mode ultrasound and IVUS provide accurate representation of the arterial wall and plaque

Identification of vulnerable plaques comes from the correct modeling of tissue echo- morphology and characterization of its composition

Under particular conditions, pixel intensity observations belonging to ultrasound images are well modeled by Rayleigh probability density functions (pdfs)

Proposal:

to characterize the echo-morphology of plaques by use of a mixture of Rayleigh distributions

to incorporate the Rayleigh Mixture Model (RMM) in a 3-type plaque classification problem

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

A plaque (as other tissue) can be regarded as a complex structure (see Fig.1) where phenomena, including absorption, diffuse and structural scattering, occur and combine

Figure 1. Tissue Acoustic model

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

Figure 2. Effect of the Rayleigh reflectivity parameter on the pdf

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

Simulation study for testing the RMM in a synthetic image

Figure 3. a) Tissue sample and b) diagonal D intensity profile. c) MLE of the Rayleigh pdf for region S, and d) comparison between MLE Rayleigh pdf and mixture pdf for the whole tissue sample

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

Figure 4. a) IVUS data acquisition and analysis from a post-mortem human coronary artery. B) Histological analysis of a slice of the artery. (c) a reliable correspondence in the IVUS image is established by using a suitable labeling software. (d) Rotation catheter, (e) Polar vs reconstructed IVUS image

(d)

(e)

Plaque characterization is based on an IVUS study of the coronary arteries Features are based on images reconstructed from the RF data 67 plaques were labeled according to their composition as lipidic, fibrotic or calcified

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

(a) (b) (c)

(d) (e) (f) Figure 5. a) IVUS image showing three plaques (tissues) labeled according to their composition. (b-c) De-speckle and

speckle image (the regularization effect is visible). (d-e) RMM estimated from the three labeled distinct plaques

RMM estimation for 3 different plaques, generation of de-speckled and speckle images

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

Figure 6. a) Feature space, where the dataset of 67 plaques of different types is plotted according to the mixture coefficients. b) 3-type plaque-content characterization using RMM computed with different number of mixture components

Performance was evaluated by means of the Leave-One-Patient-Out (LOPO) cross-validation technique, using the Adaboost classifier with Error-Correcting-Output Codes (ECOC)

1st Result: Plaque-content classification using RMM features

(b)

(a)

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

Figure 7. a) Feature space, where the dataset of 67 plaques of different types is plotted according to the mixture coefficients. b) 3-type plaque-content characterization using RMM computed with different number of mixture components. c) Graphical classification

2nd Result: Local-wise classification by use of a feature set including RMM, Speckle, Textural and Spectral features

RMM Speckle Texture Spectrum

Features Weights

(b)

(a)

(c)

Introduction Rayleigh Mixture Model Plaque Classification Results Conclusions

A generic method to model the tissue echo-morphology is proposed based on the mixture of Rayleigh distributions

Our study suggests that different plaque types can be distinguished based on the coefficients (weights) and Rayleigh parameters of each distribution of the mixture

The inclusion of mixture parameters in a classification framework has shown to improve the discriminative power between different plaque types, leading to high classification accuracies

A medical supervised plaque classification tool based on RMM can be developed, given that what is suspected to be a plaque is previously segmented and provided to the algorithm

FUTURE:

Change from Rayleigh mixture to Rician mixture

Apply this “mixture concept” and its features to classification of symptomatic carotid plaques